An official website of the United States government
Tactile sensing is essential for robots to perceive and react to the environment. However, it remains a challenge to make large-scale and flexible tactile skins on robots. Industrial machine knitting provides solutions to manufacture customiz-able fabrics. Along with functional yarns, it can produce highly customizable circuits that can be made into tactile skins for robots. In this work, we present RobotSweater, a machine-knitted pressure-sensitive tactile skin that can be easily applied on robots. We design and fabricate a parameterized multi-layer tactile skin using off-the-shelf yarns, and characterize our sensor on both a flat testbed and a curved surface to show its robust contact detection, multi-contact localization, and pressure sensing capabilities. The sensor is fabricated using a well-established textile manufacturing process with a programmable industrial knitting machine, which makes it highly customizable and low-cost. The textile nature of the sensor also makes it easily fit curved surfaces of different robots and have a friendly appearance. Using our tactile skins, we conduct closed-loop control with tactile feedback for two applications: (1) human lead-through control of a robot arm, and (2) human-robot interaction with a mobile robot.
more »
« less
This content will become publicly available on May 19, 2026
GenTact Toolbox: A Computational Design Pipeline to Procedurally Generate Context-Driven 3D Printed Whole-Body Artificial Skins
Developing whole-body tactile skins for robots remains a challenging task, as existing solutions often prioritize modular, one-size-fits-all designs, which, while versatile, fail to account for the robot’s specific shape and the unique demands of its operational context. In this work, we introduce GenTact Toolbox, a computational pipeline for creating versatile wholebody tactile skins tailored to both robot shape and application domain. Our method includes procedural mesh generation for conforming to a robot’s topology, task-driven simulation to refine sensor distribution, and multi-material 3D printing for shape-agnostic fabrication. We validate our approach by creating and deploying six capacitive sensing skins on a Franka Research 3 robot arm in a human-robot interaction scenario. This work represents a shift from “one-size-fits-all” tactile sensors toward context-driven, highly adaptable designs that can be customized for a wide range of robotic systems and applications. The project website is available at https://hiro-group.ronc.one/gentacttoolbox
more »
« less
- Award ID(s):
- 2222952
- PAR ID:
- 10615178
- Publisher / Repository:
- IEEE International Conference on Robotics and Automation (ICRA) 2025
- Date Published:
- Format(s):
- Medium: X
- Location:
- Atlanta, GA, USA
- Sponsoring Org:
- National Science Foundation
More Like this
-
-
Abstract Shape-morphing robots can change their morphology to fulfill different tasks in varying environments, but existing shape-morphing capability is not embedded in a robot’s body, requiring bulky supporting equipment. Here, we report an embedded shape-morphing scheme with the shape actuation, sensing, and locking, all embedded in a robot’s body. We showcase this embedded scheme using three morphing robotic systems: 1) self-sensing shape-morphing grippers that can adapt to objects for adaptive grasping; 2) a quadrupedal robot that can morph its body shape for different terrestrial locomotion modes (walk, crawl, or horizontal climb); 3) an untethered robot that can morph its limbs’ shape for amphibious locomotion. We also create a library of embedded morphing modules to demonstrate the versatile programmable shapes (e.g., torsion, 3D bending, surface morphing, etc.). Our embedded morphing scheme offers a promising avenue for robots to reconfigure their morphology in an embedded manner that can adapt to different environments on demand.more » « less
-
null (Ed.)This paper proposes and evaluates the use of image classification for detailed, full-body human-robot tactile interaction. A camera positioned below a translucent robot skin captures shadows generated from human touch and infers social gestures from the captured images. This approach enables rich tactile interaction with robots without the need for the sensor arrays used in traditional social robot tactile skins. It also supports the use of touch interaction with non-rigid robots, achieves high-resolution sensing for robots with different sizes and shape of surfaces, and removes the requirement of direct contact with the robot. We demonstrate the idea with an inflatable robot and a standing-alone testing device, an algorithm for recognizing touch gestures from shadows that uses Densely Connected Convolutional Networks, and an algorithm for tracking positions of touch and hovering shadows. Our experiments show that the system can distinguish between six touch gestures under three lighting conditions with 87.5 - 96.0% accuracy, depending on the lighting, and can accurately track touch positions as well as infer motion activities in realistic interaction conditions. Additional applications for this method include interactive screens on inflatable robots and privacy-maintaining robots for the home.more » « less
-
null (Ed.)Abstract Computer simulation can be a useful tool when designing robots expected to operate independently in unstructured environments. In this context, one needs to simulate the dynamics of the robot’s mechanical system, the environment in which the robot operates, and the sensors which facilitate the robot’s perception of the environment. Herein, we focus on the sensing simulation task by presenting a virtual sensing framework built alongside an open-source, multi-physics simulation platform called Chrono. This framework supports camera, lidar, GPS, and IMU simulation. We discuss their modeling as well as the noise and distortion implemented to increase the realism of the synthetic sensor data. We close with two examples that show the sensing simulation framework at work: one pertains to a reduced scale autonomous vehicle and the second is related to a vehicle driven in a digital replica of a Madison neighborhood.more » « less
-
Creating soft robots with sophisticated, autonomous capabilities requires these systems to possess reliable, on-line proprioception of 3D configuration through integrated soft sensors. We present a framework for predicting a soft robot’s 3D configuration via deep learning using feedback from a soft, proprioceptive sensor skin. Our framework introduces a kirigami-enabled strategy for rapidly sensorizing soft robots using off-the-shelf materials, a general kinematic description for soft robot geometry, and an investigation of neural network designs for predicting soft robot configuration. Even with hysteretic, non-monotonic feedback from the piezoresistive sensors, recurrent neural networks show potential for predicting our new kinematic parameters and, thus, the robot’s configuration. One trained neural network closely predicts steady-state configuration during operation, though complete dynamic behavior is not fully captured. We validate our methods on a trunk-like arm with 12 discrete actuators and 12 proprioceptive sensors. As an essential advance in soft robotic perception, we anticipate our framework will open new avenues towards closed loop control in soft robotics.more » « less
